Currently AI agents have one major limitation: they cannot learn. I mean they don't learn from their experience or from the results of completed tasks. Once the model is trained, all we can do is to tune our prompts or enrich results with domain data from RAG.
Researchers from Google started exploring how to overcome this limitation and introduced the concept called ReasoningBank.
The overall idea is simple:
1. The agent writes down the result of successful or failed tasks into a dedicated md file.
2. During task execution, the agent searches the ReasoningBank and pulls relevant memories into the context.
3. Then it uses an LLM-as-a-judge approach to self-evaluate the result, analyze the trajectory of reasoning, and extract success insights or failure reasons.
Each file has the following structure (very similar to skills):
- Title: identifier of the core strategy.
- Description: short summary of the memory item.
- Content: reasoning steps, decision explanation, or operational insights extracted from past experience.
To be honest, benchmark results compared to other agent memory approaches do not look extremely impressive:
ReasoningBank without scaling outperformed memory-free agents by 8.3% on WebArena and 4.6% on SWE-Bench-Verified.
At the same time, this approach adds even more data to the context. And context, as we know, directly affects both model behavior quality and usage cost.
The official paper contains interesting research details, including particular prompts and measurements.
From my perspective, the idea and its implementation are very similar to skills or other long-term agent memories (e.g. in Claude Code). But the overall direction of making agents capable of learning from their own experience looks really promising.
#ai #engineering #news
